Skip to content
notebook
Everyone Can Learn Python Scholarship
1️⃣ Python 🐍 - CO2 Emissions
Now let's now move on to the competition and challenge.
📖 Background
You volunteer for a public policy advocacy organization in Canada, and your colleague asked you to help her draft recommendations for guidelines on CO2 emissions rules.
After researching emissions data for a wide range of Canadian vehicles, she would like you to investigate which vehicles produce lower emissions.
💾 The data I
You have access to seven years of CO2 emissions data for Canadian vehicles (source):
- "Make" - The company that manufactures the vehicle.
- "Model" - The vehicle's model.
- "Vehicle Class" - Vehicle class by utility, capacity, and weight.
- "Engine Size(L)" - The engine's displacement in liters.
- "Cylinders" - The number of cylinders.
- "Transmission" - The transmission type: A = Automatic, AM = Automatic Manual, AS = Automatic with select shift, AV = Continuously variable, M = Manual, 3 - 10 = the number of gears.
- "Fuel Type" - The fuel type: X = Regular gasoline, Z = Premium gasoline, D = Diesel, E = Ethanol (E85), N = natural gas.
- "Fuel Consumption Comb (L/100 km)" - Combined city/highway (55%/45%) fuel consumption in liters per 100 km (L/100 km).
- "CO2 Emissions(g/km)" - The tailpipe carbon dioxide emissions in grams per kilometer for combined city and highway driving.
The data comes from the Government of Canada's open data website.
# Import the pandas and numpy packages
import pandas as pd
import numpy as np
# Load the data
cars = pd.read_csv('data/co2_emissions_canada.csv')
# create numpy arrays
cars_makes = cars['Make'].to_numpy()
cars_models = cars['Model'].to_numpy()
cars_classes = cars['Vehicle Class'].to_numpy()
cars_engine_sizes = cars['Engine Size(L)'].to_numpy()
cars_cylinders = cars['Cylinders'].to_numpy()
cars_transmissions = cars['Transmission'].to_numpy()
cars_fuel_types = cars['Fuel Type'].to_numpy()
cars_fuel_consumption = cars['Fuel Consumption Comb (L/100 km)'].to_numpy()
cars_co2_emissions = cars['CO2 Emissions(g/km)'].to_numpy()
# Preview the dataframe
cars
💪 Challenge I
Help your colleague gain insights on the type of vehicles that have lower CO2 emissions. Include:
- What is the median engine size in liters?
- What is the average fuel consumption for regular gasoline (Fuel Type = X), premium gasoline (Z), ethanol (E), and diesel (D)?
- What is the correlation between fuel consumption and CO2 emissions?
- Which vehicle class has lower average CO2 emissions, 'SUV - SMALL' or 'MID-SIZE'?
- What are the average CO2 emissions for all vehicles? For vehicles with an engine size of 2.0 liters or smaller?
- Any other insights you found during your analysis?
# What is the median engine size in liters
engine_median = cars['Engine Size(L)'].median()
print("The median engine size in liters is {}".format(engine_median))
# What is the average fuel consumption for regular gasoline
avg_fuel = cars.groupby(cars["Fuel Type"])["Fuel Consumption Comb (L/100 km)"].mean()
print("The average fuel consumption for regular gasoline is {} ".format(avg_fuel["X"]))
# What is the correlation between fuel consumption and CO2 emissions?
corr = cars["Fuel Consumption Comb (L/100 km)"].corr(cars["CO2 Emissions(g/km)"])
print("The correlation between fuel consumption and co2 emission is {}".format(corr))
correlation = cars[["Fuel Consumption Comb (L/100 km)", "CO2 Emissions(g/km)"]].corr()
import seaborn as sns
sns.heatmap(correlation, xticklabels=correlation.columns, yticklabels=correlation.columns, annot=True)
# Which vehicle class has lower average CO2 emissions, 'SUV - SMALL' or 'MID-SIZE'?
emission = cars.groupby(cars["Vehicle Class"])["CO2 Emissions(g/km)"].mean()
emission[["SUV - SMALL", "MID-SIZE"]]
print("(MID-SIZE) Vehicle class has a lower average co2 emission than (SUV-SMALL) vehicle size.")
# What are the average CO2 emissions for all vehicles? For vehicles with an engine size of 2.0 liters or smaller?
avg_emission = cars.groupby(["Engine Size(L)"])["CO2 Emissions(g/km)"].mean().reset_index(name="Average co2 emission")
avg_emission.loc[0:8]